Database-friendly random projections

A classic result of Johnson and Lindenstrauss asserts that any set of n points in d-dimensional Euclidean space can be embedded into k-dimensional Euclidean space -- where k is logarithmic in n and independent of d -- so that all pairwise distances are maintained within an arbitrarily small factor. All known constructions of such embeddings involve projecting the n points onto a random k-dimensional hyperplane. We give a novel construction of the embedding, suitable for database applications, which amounts to computing a simple aggregate over k random attribute partitions.

• Type: Conference
• Year: 2001
• Where: PODS
• Authors: Dimitris Achlioptas
• Keywords: D-dimensional Euclidean Space, Database, Euclidean Space, PODS 2001, Random Attribute Partitions